Rank–rank hypergeometric overlap: identification of statistically significant overlap between gene-expression signatures

Comparing independent high-throughput gene-expression experiments can generate hypotheses about which gene-expression programs are shared between particular biological processes. Current techniques to compare expression profiles typically involve choosing a fixed differential expression threshold to summarize results, potentially reducing sensitivity to small but concordant changes. We present a threshold-free algorithm called Rank–rank Hypergeometric Overlap (RRHO). This algorithm steps through two gene lists ranked by the degree of differential expression observed in two profiling experiments, successively measuring the statistical significance of the number of overlapping genes. The output is a graphical map that shows the strength, pattern and bounds of correlation between two expression profiles. To demonstrate RRHO sensitivity and dynamic range, we identified shared expression networks in cancer microarray profiles driving tumor progression, stem cell properties and response to targeted kinase inhibition. We demonstrate how RRHO can be used to determine which model system or drug treatment best reflects a particular biological or disease response. The threshold-free and graphical aspects of RRHO complement other rank-based approaches such as Gene Set Enrichment Analysis (GSEA), for which RRHO is a 2D analog. Rank–rank overlap analysis is a sensitive, robust and web-accessible method for detecting and visualizing overlap trends between two complete, continuous gene-expression profiles. A web-based implementation of RRHO can be accessed at http://systems.crump.ucla.edu/rankrank/

Rank-rank hypergeometric overlap: identification of statistically significant overlap between gene-expression signatures.

Comparing independent high-throughput gene-expression experiments can generate hypotheses about which gene-expression programs are shared between particular biological processes. Current techniques to compare expression profiles typically involve choosing a fixed differential expression threshold to summarize results, potentially reducing sensitivity to small but concordant changes. We present a threshold-free algorithm called Rank-rank Hypergeometric Overlap (RRHO). This algorithm steps through two gene lists ranked by the degree of differential expression observed in two profiling experiments, successively measuring the statistical significance of the number of overlapping genes. The output is a graphical map that shows the strength, pattern and bounds of correlation between two expression profiles. To demonstrate RRHO sensitivity and dynamic range, we identified shared expression networks in cancer microarray profiles driving tumor progression, stem cell properties and response to targeted kinase inhibition. We demonstrate how RRHO can be used to determine which model system or drug treatment best reflects a particular biological or disease response. The threshold-free and graphical aspects of RRHO complement other rank-based approaches such as Gene Set Enrichment Analysis (GSEA), for which RRHO is a 2D analog. Rank-rank overlap analysis is a sensitive, robust and web-accessible method for detecting and visualizing overlap trends between two complete, continuous gene-expression profiles. A web-based implementation of RRHO can be accessed at http://systems.crump.ucla.edu/rankrank/

Performance Evaluation of G8, a High-Sensitivity Benchtop Preclinical PET/CT Tomograph

G8 is a benchtop integrated PET/CT scanner dedicated to high-sensitivity and high-resolution imaging of mice. This work characterizes its National Electrical Manufacturers Association NU 4-2008 performance where applicable and also assesses the basic imaging performance of the CT subsystem. Methods: The PET subsystem in G8 consists of 4 flat-panel detectors arranged in a boxlike geometry. Each panel consists of 2 modules of a 26 × 26 pixelated bismuth germanate scintillator array with individual crystals measuring 1.75 × 1.75 × 7.2 mm. The crystal arrays are coupled to multichannel photomultiplier tubes via a tapered, pixelated glass lightguide. A cone-beam CT scanner consisting of a MicroFocus x-ray source and a complementary metal oxide semiconductor detector provides anatomic information. Sensitivity, spatial resolution, energy resolution, scatter fraction, count-rate performance, and the capability of performing phantom and mouse imaging were evaluated for the PET subsystem. Noise, dose level, contrast, and resolution were evaluated for the CT subsystem. Results: With an energy window of 350-650 keV, the peak sensitivity was 9.0% near the center of the field of view. The crystal energy resolution ranged from 15.0% to 69.6% in full width at half maximum (FWHM), with a mean of 19.3% ± 3.7%. The average intrinsic spatial resolution was 1.30 and 1.38 mm FWHM in the transverse and axial directions, respectively. The maximum-likelihood expectation maximization reconstructed image of a point source in air averaged 0.81 ± 0.11 mm FWHM. The peak noise-equivalent count rate for the mouse-sized phantom was 44 kcps for a total activity of 2.9 MBq (78 μCi), and the scatter fraction was 11%. For the CT subsystem, the value of the modulation transfer function at 10% was 2.05 cycles/mm. Conclusion: The overall performance demonstrates that the G8 can produce high-quality images for molecular imaging-based biomedical research

Performance Evaluation of G8, a High-Sensitivity Benchtop Preclinical PET/CT Tomograph

G8 is a benchtop integrated PET/CT scanner dedicated to high-sensitivity and high-resolution imaging of mice. This work characterizes its National Electrical Manufacturers Association NU 4-2008 performance where applicable and also assesses the basic imaging performance of the CT subsystem. Methods: The PET subsystem in G8 consists of 4 flat-panel detectors arranged in a boxlike geometry. Each panel consists of 2 modules of a 26 × 26 pixelated bismuth germanate scintillator array with individual crystals measuring 1.75 × 1.75 × 7.2 mm. The crystal arrays are coupled to multichannel photomultiplier tubes via a tapered, pixelated glass lightguide. A cone-beam CT scanner consisting of a MicroFocus x-ray source and a complementary metal oxide semiconductor detector provides anatomic information. Sensitivity, spatial resolution, energy resolution, scatter fraction, count-rate performance, and the capability of performing phantom and mouse imaging were evaluated for the PET subsystem. Noise, dose level, contrast, and resolution were evaluated for the CT subsystem. Results: With an energy window of 350-650 keV, the peak sensitivity was 9.0% near the center of the field of view. The crystal energy resolution ranged from 15.0% to 69.6% in full width at half maximum (FWHM), with a mean of 19.3% ± 3.7%. The average intrinsic spatial resolution was 1.30 and 1.38 mm FWHM in the transverse and axial directions, respectively. The maximum-likelihood expectation maximization reconstructed image of a point source in air averaged 0.81 ± 0.11 mm FWHM. The peak noise-equivalent count rate for the mouse-sized phantom was 44 kcps for a total activity of 2.9 MBq (78 μCi), and the scatter fraction was 11%. For the CT subsystem, the value of the modulation transfer function at 10% was 2.05 cycles/mm. Conclusion: The overall performance demonstrates that the G8 can produce high-quality images for molecular imaging-based biomedical research